Diverse Motions and Responses in Crowd Simulation

• Published in: Computer animation and virtual worlds Vol. 35; no. 6
• Main Authors: Ma, Yiwen, Liu, Tingting, Liu, Zhen
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.11.2024; Wiley Subscription Services, Inc
• Subjects: Algorithms; Animation; character animation; Collision avoidance; crowd simulation; Learning; motion synthesis; Movement; Pedestrians; physics‐based animation; Realism; Simulation; Virtual environments; Visual aspects
• ISSN: 1546-4261; 1546-427X
• DOI: 10.1002/cav.70002

ABSTRACT A challenge in crowd simulation is to generate diverse pedestrian motions in virtual environments. Nowadays, there is a greater emphasis on the diversity and authenticity of pedestrian movements in crowd simulation, while most traditional models primarily focus on collision avoidance and motion continuity. Recent studies have enhanced realism through data‐driven approaches that exploit the movement patterns of pedestrians from real data for trajectory prediction. However, they have not taken into account the body‐part motions of pedestrians. Differing from these approaches, we innovatively utilize learning‐based character motion and physics animation to enhance the diversity of pedestrian motions in crowd simulation. The proposed method can provide a promising avenue for more diverse crowds and is realized by a novel framework that deeply integrates motion synthesis and physics animation with crowd simulation. The framework consists of three main components: the learning‐based motion generator, which is responsible for generating diverse character motions; the hybrid simulation, which ensures the physical realism of pedestrian motions; and the velocity‐based interface, which assists in integrating navigation algorithms with the motion generator. Experiments have been conducted to verify the effectiveness of the proposed method in different aspects. The visual results demonstrate the feasibility of our approach. We propose a novel framework for crowd simulation that deeply integrates learning‐based character motion and physics‐based character animation with crowd navigation algorithms.